Meeting Title: CTA | Tools Set up Date: 2026-04-22 Meeting participants: Awaish Kumar, Chris Terry, Amber Lin
WEBVTT
1 00:00:16.219 ⇒ 00:00:17.350 Chris Terry: Afternoons.
2 00:00:19.660 ⇒ 00:00:20.350 Awaish Kumar: Hello.
3 00:00:24.270 ⇒ 00:00:25.469 Chris Terry: Feeling alright today?
4 00:00:29.570 ⇒ 00:00:30.380 Awaish Kumar: Sorry.
5 00:00:36.470 ⇒ 00:00:38.390 Awaish Kumar: Hey, how you doing, Chris?
6 00:00:39.080 ⇒ 00:00:40.370 Chris Terry: Going pretty good, can you hear me?
7 00:00:41.240 ⇒ 00:00:42.860 Awaish Kumar: Yeah, it’s really slow.
8 00:01:03.230 ⇒ 00:01:04.590 Chris Terry: Can you hear everybody now?
9 00:01:04.989 ⇒ 00:01:06.099 Awaish Kumar: Yeah, yeah.
10 00:01:06.860 ⇒ 00:01:07.680 Chris Terry: Okay, cool.
11 00:01:09.930 ⇒ 00:01:11.199 Awaish Kumar: Okay, -oh.
12 00:01:14.730 ⇒ 00:01:18.490 Awaish Kumar: Yeah, like, can you tell me, like, do you have access to everything?
13 00:01:19.020 ⇒ 00:01:22.420 Awaish Kumar: There you go, although it is really stated in the Slack.
14 00:01:23.490 ⇒ 00:01:31.629 Chris Terry: Yeah, I think so now. Yesterday I was working on getting access to the GitHub and the repo, so I should be good now.
15 00:01:33.580 ⇒ 00:01:39.280 Awaish Kumar: Okay, so you have, repo. Okay, can you share your screen?
16 00:01:40.490 ⇒ 00:01:41.300 Chris Terry: Certainly.
17 00:01:46.360 ⇒ 00:01:47.640 Chris Terry: I’m getting the right one.
18 00:01:58.650 ⇒ 00:02:00.789 Chris Terry: And I also have a cursor up here as well.
19 00:02:02.140 ⇒ 00:02:06.210 Awaish Kumar: Okay, so you have… Cursor?
20 00:02:07.150 ⇒ 00:02:07.780 Chris Terry: Yes.
21 00:02:08.360 ⇒ 00:02:12.010 Awaish Kumar: Okay, so you are using Guster and not the Cortex mode.
22 00:02:13.720 ⇒ 00:02:18.330 Chris Terry: Bum… I haven’t messed with Cortex yet, I’ve been mainly doing…
23 00:02:18.330 ⇒ 00:02:24.929 Awaish Kumar: Like, we are also using Casser, but, Catherine or Kyle are using Codex Code.
24 00:02:26.020 ⇒ 00:02:27.210 Chris Terry: I gotcha, I gotcha.
25 00:02:28.600 ⇒ 00:02:32.290 Chris Terry: Yeah, I was just pulling up some of the tables, I wanted to kind of see what they looked like, so…
26 00:02:32.880 ⇒ 00:02:35.199 Awaish Kumar: Yeah, so basically this is the…
27 00:02:35.560 ⇒ 00:02:44.180 Awaish Kumar: you are in the correct, project and the folders. This is where we are writing all our models.
28 00:02:44.650 ⇒ 00:02:51.320 Awaish Kumar: So… So, the flow is, right now, I…
29 00:02:51.640 ⇒ 00:02:57.720 Awaish Kumar: the… Catherine or Kyle, they write some ingestion pipelines, either through Fivetran or…
30 00:02:57.910 ⇒ 00:03:00.650 Awaish Kumar: Writing some, lambda function.
31 00:03:00.870 ⇒ 00:03:03.629 Awaish Kumar: And then it ends up in S3.
32 00:03:03.770 ⇒ 00:03:08.669 Awaish Kumar: If you have access to S3, I can show you where it goes.
33 00:03:10.640 ⇒ 00:03:14.679 Chris Terry: Let me see if I can find out… let me move this. Out of my way.
34 00:03:16.700 ⇒ 00:03:18.130 Chris Terry: Puff, buff, I think…
35 00:03:18.130 ⇒ 00:03:18.690 Awaish Kumar: Oh, what?
36 00:03:19.580 ⇒ 00:03:20.350 Chris Terry: Here.
37 00:03:22.590 ⇒ 00:03:24.159 Awaish Kumar: Yeah, you can use power.
38 00:03:24.410 ⇒ 00:03:26.990 Awaish Kumar: User… Oh, no, no.
39 00:03:26.990 ⇒ 00:03:27.699 Chris Terry: How are you?
40 00:03:27.700 ⇒ 00:03:29.209 Awaish Kumar: Get an old power user.
41 00:03:29.690 ⇒ 00:03:30.250 Chris Terry: Alright.
42 00:03:34.250 ⇒ 00:03:35.659 Awaish Kumar: And there is 3.
43 00:03:36.920 ⇒ 00:03:37.540 Chris Terry: Alright.
44 00:03:38.710 ⇒ 00:03:44.400 Awaish Kumar: Okay, and then if you look at the data lake, Oh…
45 00:03:46.660 ⇒ 00:03:47.729 Chris Terry: Oh, and here.
46 00:03:50.220 ⇒ 00:03:53.489 Awaish Kumar: Yeah, this is called CT Data House Playground Data Lake.
47 00:03:54.400 ⇒ 00:03:55.179 Chris Terry: There it is.
48 00:03:55.960 ⇒ 00:03:59.480 Awaish Kumar: So, this is where all the data goes.
49 00:03:59.700 ⇒ 00:04:03.820 Awaish Kumar: If whenever they…
50 00:04:04.860 ⇒ 00:04:14.389 Awaish Kumar: Yeah, whenever there is any ingestion pipeline, the data goes in there, specifically in the raw. So other things are, like, data governance is mainly we…
51 00:04:14.520 ⇒ 00:04:16.130 Awaish Kumar: I don’t have,
52 00:04:16.540 ⇒ 00:04:22.890 Awaish Kumar: like, any standard mappings and things like that, but the data that is ingested goes into RAW.
53 00:04:23.460 ⇒ 00:04:27.730 Awaish Kumar: If you look at the raw, there is a lot of different tabs.
54 00:04:28.910 ⇒ 00:04:31.369 Chris Terry: And so where do these, like, get brought in from?
55 00:04:32.900 ⇒ 00:04:33.640 Awaish Kumar: Sorry?
56 00:04:34.190 ⇒ 00:04:36.790 Chris Terry: Where does, like, the information come from, generally?
57 00:04:38.500 ⇒ 00:04:41.290 Awaish Kumar: information regarding… In addition.
58 00:04:41.290 ⇒ 00:04:44.229 Chris Terry: Like, the sor… yeah, like, all these different sources, or whatever.
59 00:04:45.150 ⇒ 00:04:52.990 Awaish Kumar: Information comes from Catherine, right? She’s the… and Kyle, like, they are the ones who… who know about a lot of systems.
60 00:04:53.210 ⇒ 00:04:58.400 Awaish Kumar: We use in the company, and also they know about,
61 00:04:59.190 ⇒ 00:05:05.119 Awaish Kumar: Then, like, they meet with the new teams in the company and figure out, like, where the data lives.
62 00:05:05.410 ⇒ 00:05:11.219 Awaish Kumar: For example, they are going to meet with the marketing team to understand what, different,
63 00:05:11.580 ⇒ 00:05:19.909 Awaish Kumar: platforms they’re using for campaign, and then basically, based on that, they will try to get the access and then bring in the data.
64 00:05:20.900 ⇒ 00:05:23.279 Chris Terry: Yeah, okay, that makes sense. Yeah, yeah.
65 00:05:23.630 ⇒ 00:05:26.780 Awaish Kumar: Yeah, so right now, what’s happening is, in the raw.
66 00:05:26.900 ⇒ 00:05:32.660 Awaish Kumar: Mostly, Kyle and Catherine are the ones that… they bring in the data here.
67 00:05:32.850 ⇒ 00:05:36.569 Awaish Kumar: Either through FiveTrad, or it comes through,
68 00:05:36.830 ⇒ 00:05:42.650 Awaish Kumar: they write some Python scripts, no, they don’t… I mean, AWS Lambda functions.
69 00:05:42.850 ⇒ 00:05:50.270 Awaish Kumar: And then… Oh… Once it is here, then we… we take it over,
70 00:05:50.380 ⇒ 00:05:58.289 Awaish Kumar: in a sense that we write the further ingestion and the modeling in Snowflake, so I… we basically write in the…
71 00:05:58.520 ⇒ 00:06:01.130 Awaish Kumar: write the… Oh…
72 00:06:02.390 ⇒ 00:06:12.290 Awaish Kumar: Snowpipes. So, in Snowflake, there is a feature called Snowpipes. Using that, you can basically bring in data from S3 into Snowflake in real time.
73 00:06:12.920 ⇒ 00:06:14.090 Awaish Kumar: So if you…
74 00:06:14.090 ⇒ 00:06:14.510 Chris Terry: Oh.
75 00:06:14.510 ⇒ 00:06:15.450 Awaish Kumar: No.
76 00:06:16.680 ⇒ 00:06:17.860 Awaish Kumar: database.
77 00:06:19.490 ⇒ 00:06:21.120 Chris Terry: What I searched for…
78 00:06:21.410 ⇒ 00:06:23.140 Awaish Kumar: Just call raw, yeah.
79 00:06:31.090 ⇒ 00:06:40.360 Awaish Kumar: So there is a setting, setup required, like, there’s, like, data inti… like, S3 integration, is required.
80 00:06:40.520 ⇒ 00:06:49.020 Awaish Kumar: For that, you have to provide some IAM rules and things like that, but once that is done, whatever is in,
81 00:06:49.540 ⇒ 00:06:56.900 Awaish Kumar: raw. So you can see, if you click on, for example, Salesforce Marketing Cloud.
82 00:07:01.290 ⇒ 00:07:05.949 Awaish Kumar: You see, there are tables, there are stages, there are file formats, and there are pipes.
83 00:07:08.480 ⇒ 00:07:15.690 Awaish Kumar: Okay, so these are the types that we have created which are responsible for flowing the data in from S3.
84 00:07:17.660 ⇒ 00:07:20.369 Chris Terry: Also, yeah, they’re being loaded,
85 00:07:20.670 ⇒ 00:07:24.679 Chris Terry: Some of these get loaded, I guess, every once in a while, and some of them are more common than others.
86 00:07:25.050 ⇒ 00:07:31.430 Awaish Kumar: They are… these are basically… Doing in real time, so whenever a file lands, they will run.
87 00:07:31.540 ⇒ 00:07:32.270 Awaish Kumar: They’re not…
88 00:07:32.270 ⇒ 00:07:33.020 Chris Terry: Whoa.
89 00:07:33.240 ⇒ 00:07:36.830 Awaish Kumar: They’re not on a schedule, they are just, like, .
90 00:07:37.320 ⇒ 00:07:38.170 Chris Terry: Automatic.
91 00:07:38.170 ⇒ 00:07:44.399 Awaish Kumar: Yeah, streaming. When a file loads in, So, S3 sends an event.
92 00:07:44.660 ⇒ 00:07:54.370 Awaish Kumar: that, to a queue, that, okay, I have received something, and based… based on that, this pipe will execute, and it will bring in the data.
93 00:07:55.310 ⇒ 00:07:56.580 Chris Terry: like a near-live…
94 00:07:57.230 ⇒ 00:07:57.900 Awaish Kumar: Sorry?
95 00:07:58.310 ⇒ 00:08:02.070 Chris Terry: It’s, like, near live information, or live information.
96 00:08:02.070 ⇒ 00:08:04.930 Awaish Kumar: Real time, you can say, real time, basically.
97 00:08:04.930 ⇒ 00:08:05.949 Chris Terry: Yeah, perfect, perfect.
98 00:08:06.220 ⇒ 00:08:07.400 Chris Terry: Yeah, there’s no…
99 00:08:07.400 ⇒ 00:08:15.200 Awaish Kumar: Lit and simple game between… So it will break it in a few seconds, right? Yeah.
100 00:08:15.510 ⇒ 00:08:18.179 Awaish Kumar: 46 seconds, it is just loaded.
101 00:08:18.300 ⇒ 00:08:19.499 Awaish Kumar: Every day.
102 00:08:19.760 ⇒ 00:08:20.420 Awaish Kumar: So…
103 00:08:20.420 ⇒ 00:08:21.120 Chris Terry: That’s awesome.
104 00:08:22.220 ⇒ 00:08:40.979 Awaish Kumar: it’s always… so these are the pipes that we’ve created. We do that for ingesting the data from S3 to Snowflake. Once it is ingested, you see our dbt project? That is basically creating all other, like, the models. So once it is in a raw,
105 00:08:41.070 ⇒ 00:08:43.679 Awaish Kumar: We don’t touch anything,
106 00:08:44.400 ⇒ 00:08:52.130 Awaish Kumar: outside of our dbt project. We go into our dbt repo that you already opened, Right? Previously, in cursor.
107 00:08:52.810 ⇒ 00:08:53.810 Chris Terry: Oh, go.
108 00:08:54.410 ⇒ 00:09:01.489 Awaish Kumar: So this is our dbt project, and here we… Do all the modeling, so…
109 00:09:01.750 ⇒ 00:09:10.129 Awaish Kumar: The only step that is outside of this repo is ingesting to Snowflake, using snowpipes. That is how it’s happening
110 00:09:10.530 ⇒ 00:09:19.089 Awaish Kumar: Separately. But apart from that, all the models we create is… are created using this repository. So, you can open the raw.
111 00:09:21.550 ⇒ 00:09:22.990 Awaish Kumar: I, aye, like…
112 00:09:24.690 ⇒ 00:09:43.540 Awaish Kumar: Yeah, these are some of the tables that are in the snowflake. We are just referencing it here as an ephemeral model, so it does not create anything, it just references it, so it’s just here for readability, but then we’re staging models, and then we create intermediate models.
113 00:09:44.090 ⇒ 00:09:44.610 Awaish Kumar: So…
114 00:09:44.610 ⇒ 00:09:49.739 Chris Terry: question, because I haven’t used this before, is run… I assume that just runs whatever I’ve got here.
115 00:09:53.940 ⇒ 00:10:01.410 Awaish Kumar: It… you need to do some configuration. This run is basically to run for the, like, in a context of
116 00:10:01.930 ⇒ 00:10:08.840 Awaish Kumar: cursor, that’s why it is created like that, but without… it won’t work without configuration.
117 00:10:09.400 ⇒ 00:10:13.249 Awaish Kumar: So to run… if you want to execute this model.
118 00:10:13.440 ⇒ 00:10:17.970 Awaish Kumar: You have to run dbt projects. Have you ever worked with… before with dbt?
119 00:10:19.220 ⇒ 00:10:21.430 Chris Terry: Not, not significantly, no.
120 00:10:21.430 ⇒ 00:10:23.080 Awaish Kumar: But you can utilize this.
121 00:10:23.610 ⇒ 00:10:28.339 Awaish Kumar: Okay, so… I think, then, have you used Cassar before?
122 00:10:29.050 ⇒ 00:10:30.090 Chris Terry: No, I’m not.
123 00:10:30.910 ⇒ 00:10:36.239 Awaish Kumar: So… I don’t think that is going to work for you, because…
124 00:10:36.240 ⇒ 00:10:36.790 Chris Terry: Huh.
125 00:10:37.280 ⇒ 00:10:44.030 Awaish Kumar: like… Because I think the CTA team has, don’t have any…
126 00:10:44.240 ⇒ 00:10:49.190 Awaish Kumar: membership for CASA, so you will be limited by free version.
127 00:10:49.360 ⇒ 00:10:56.330 Awaish Kumar: Right? You need… I think you should set up Cortex and ask Catherine for…
128 00:10:56.530 ⇒ 00:11:01.739 Awaish Kumar: Like, the… how you can… how… maybe she can add you as a team member or something.
129 00:11:02.510 ⇒ 00:11:03.410 Chris Terry: Yeah, yeah.
130 00:11:03.870 ⇒ 00:11:07.219 Chris Terry: So I could probably… I could potentially do that through Snowflake?
131 00:11:09.430 ⇒ 00:11:13.999 Awaish Kumar: you can download the Cortex Cloud. You can just search for Cortex Cloud.
132 00:11:14.630 ⇒ 00:11:15.250 Chris Terry: Okay.
133 00:11:15.650 ⇒ 00:11:16.800 Awaish Kumar: You can download it.
134 00:11:17.940 ⇒ 00:11:18.659 Chris Terry: I gotcha.
135 00:11:20.910 ⇒ 00:11:25.549 Awaish Kumar: And then, I think she needs to add you to some team.
136 00:11:25.780 ⇒ 00:11:32.719 Awaish Kumar: Some… somehow you need to upgrade from the free version to a… A provision, or something.
137 00:11:33.510 ⇒ 00:11:34.150 Chris Terry: I got you.
138 00:11:36.200 ⇒ 00:11:45.539 Awaish Kumar: So this is the structure, and you have to set it up to examine it, but I think you should now set up the cortex first, since you will be working in there.
139 00:11:45.890 ⇒ 00:11:47.940 Awaish Kumar: And then we can set up in there.
140 00:11:50.190 ⇒ 00:11:50.900 Chris Terry: I gotcha.
141 00:11:51.400 ⇒ 00:11:52.030 Awaish Kumar: Yeah.
142 00:11:54.000 ⇒ 00:11:57.109 Chris Terry: So, get that Vortex, downloaded.
143 00:11:57.340 ⇒ 00:11:58.790 Chris Terry: I forgot that in my notes.
144 00:12:04.330 ⇒ 00:12:06.359 Chris Terry: I’ll be chatting with her later today.
145 00:12:06.490 ⇒ 00:12:08.020 Chris Terry: I’ll mention it to her.
146 00:12:12.010 ⇒ 00:12:17.210 Chris Terry: Very cool. Okay, yeah, raw data, got staging,
147 00:12:17.600 ⇒ 00:12:22.609 Chris Terry: So I guess this does a little bit of cleanup, and then intermediate is also further cleanup, maybe?
148 00:12:22.990 ⇒ 00:12:27.160 Awaish Kumar: Yeah, this is the structure of dbt. There are different layers.
149 00:12:27.880 ⇒ 00:12:28.490 Chris Terry: Oh, yeah.
150 00:12:28.490 ⇒ 00:12:42.870 Awaish Kumar: It goes staging, where you do some cleanups, then in intermediate, then you do some further transformation, joins and everything, and then it goes to March, where you have final March table that are basically exposed to the end users.
151 00:12:43.500 ⇒ 00:12:49.430 Chris Terry: Right, right, that makes sense. That’s my understanding of it as well, so… Oh, Cortex adoption, okay.
152 00:12:50.630 ⇒ 00:12:52.290 Chris Terry: Oh…
153 00:12:55.450 ⇒ 00:12:56.879 Awaish Kumar: Yeah, so…
154 00:13:00.780 ⇒ 00:13:08.329 Chris Terry: Very cool, very cool. That makes more sense on how all this is kind of pulled together.
155 00:13:08.850 ⇒ 00:13:14.179 Chris Terry: Where it all comes from, and all that good stuff. So, that makes a lot more sense.
156 00:13:17.570 ⇒ 00:13:21.810 Awaish Kumar: Okay, I think then what you need is, you need to set up Cortex.
157 00:13:22.940 ⇒ 00:13:25.110 Awaish Kumar: First of all, that is the…
158 00:13:25.280 ⇒ 00:13:28.570 Awaish Kumar: Minimum requirement to basically get started.
159 00:13:30.890 ⇒ 00:13:36.940 Awaish Kumar: set it up, ask Catherine for how you can go to the pro version, Then we can…
160 00:13:37.710 ⇒ 00:13:47.569 Awaish Kumar: open the repository in Codex Core, and then we can work in there, like, you can ask Cortex to set up your dbt environment, you can ask.
161 00:13:47.840 ⇒ 00:13:48.490 Chris Terry: Anything.
162 00:13:48.510 ⇒ 00:13:50.119 Awaish Kumar: Yeah, so, yeah.
163 00:13:52.430 ⇒ 00:14:00.269 Chris Terry: Alright, DBT… environment. And so this… the reason that I need this as part of… is that…
164 00:14:00.650 ⇒ 00:14:08.000 Chris Terry: So it’s… I needed to be able to run this in… I’m trying to think.
165 00:14:08.000 ⇒ 00:14:14.280 Awaish Kumar: You don’t need a Cortex for running dbt. You can run dbt from any environment.
166 00:14:14.380 ⇒ 00:14:25.750 Awaish Kumar: you can run it from Cursor as well. They are just IDs. But what I’m trying to say is, this is… you’re not on a pro version in Cursor. If I…
167 00:14:25.750 ⇒ 00:14:26.409 Chris Terry: Like so.
168 00:14:26.410 ⇒ 00:14:33.829 Awaish Kumar: We are using, normally now, trying to use AI to help us with our speeding up our workflow, right?
169 00:14:34.140 ⇒ 00:14:45.470 Awaish Kumar: So instead of doing all the configuration from our hands, I can ask you, like, okay, write to the cursor that it can help you create an environment where you can run dbt.
170 00:14:45.690 ⇒ 00:14:52.860 Awaish Kumar: It can create it for you. But the only problem is that you are on a free version, and it will stop after a few prompts.
171 00:14:53.010 ⇒ 00:14:55.349 Awaish Kumar: That makes sense. Left midway.
172 00:14:55.500 ⇒ 00:15:04.540 Awaish Kumar: So, and you are… and I know that CTA is not using cursor, so there’s no way you can upgrade this, like, without you yourself paying for it.
173 00:15:04.720 ⇒ 00:15:08.680 Awaish Kumar: So the only way is Cortex code, that is, your team is using.
174 00:15:09.140 ⇒ 00:15:14.759 Awaish Kumar: And you can ask Catherine to give you the membership, or add you in a team, or something like that.
175 00:15:15.440 ⇒ 00:15:19.190 Chris Terry: I gotcha, I gotcha. So do you work with the DBT stuff a lot, or…
176 00:15:19.840 ⇒ 00:15:26.300 Awaish Kumar: Yeah, I work with everything what I just told you about snow pipes,
177 00:15:26.720 ⇒ 00:15:34.280 Awaish Kumar: DBT… And also, finally, from the dbt, once you are okay with running dbt, we can move…
178 00:15:34.490 ⇒ 00:15:38.269 Awaish Kumar: move to using… how to use Cortex.
179 00:15:40.730 ⇒ 00:15:44.720 Awaish Kumar: like, agents, how to use Cortex Analyst, and things like that.
180 00:15:46.870 ⇒ 00:15:47.500 Chris Terry: Okay.
181 00:15:47.500 ⇒ 00:15:53.589 Awaish Kumar: are on top of, so once we are okay with writing DBT, once we are okay with writing,
182 00:15:53.930 ⇒ 00:15:54.820 Awaish Kumar: Wow.
183 00:15:56.470 ⇒ 00:16:02.530 Awaish Kumar: Like, ingestion pipelines, and, like, once you have overview for these things, then you can also move towards,
184 00:16:02.750 ⇒ 00:16:03.590 Awaish Kumar: Cool.
185 00:16:04.480 ⇒ 00:16:10.780 Awaish Kumar: Snowflake, right? So once the data is in Snowflake, on top of it, you can run AI inside of Snowflake.
186 00:16:11.010 ⇒ 00:16:16.890 Awaish Kumar: And you can create your own, Romantic views in Snowflake.
187 00:16:17.940 ⇒ 00:16:18.610 Chris Terry: Right. Strait.
188 00:16:18.870 ⇒ 00:16:27.859 Awaish Kumar: That is… that also needs, like, environment, right? So one way is that I can show you, and you can go in SoFlink UI, you can click on
189 00:16:28.530 ⇒ 00:16:33.089 Awaish Kumar: semantic views, start creating index in the UI itself.
190 00:16:33.210 ⇒ 00:16:41.869 Awaish Kumar: You can do that. The other flow that we are trying to do is using everything using… by using code, instead of going into the snowflake.
191 00:16:43.850 ⇒ 00:16:48.799 Awaish Kumar: And to do that, you also need Cortex code. So that is, like, that’s what you have to set up.
192 00:16:50.000 ⇒ 00:16:50.510 Chris Terry: Okay.
193 00:16:50.510 ⇒ 00:16:55.000 Awaish Kumar: But if you go to the right side, right-click, no, not this one, just… Okay.
194 00:16:56.900 ⇒ 00:16:57.339 Chris Terry: on the roll.
195 00:16:59.480 ⇒ 00:17:03.130 Awaish Kumar: No, no, on the, on the bar, on the left side bar.
196 00:17:03.130 ⇒ 00:17:04.530 Chris Terry: Oh, this, yeah, yeah.
197 00:17:07.400 ⇒ 00:17:08.770 Awaish Kumar: On the left side.
198 00:17:09.180 ⇒ 00:17:10.060 Chris Terry: I love it.
199 00:17:10.060 ⇒ 00:17:13.670 Awaish Kumar: On the left, this is… there are some small icons on the left.
200 00:17:15.109 ⇒ 00:17:15.799 Chris Terry: Yep.
201 00:17:15.800 ⇒ 00:17:18.040 Awaish Kumar: Further left, yeah, these ones.
202 00:17:18.319 ⇒ 00:17:22.050 Awaish Kumar: you can go to the AIA part, move up.
203 00:17:23.619 ⇒ 00:17:30.259 Awaish Kumar: There should be, yeah, this, and you can click on the… Analyst.
204 00:17:31.210 ⇒ 00:17:31.920 Chris Terry: Alright.
205 00:17:33.170 ⇒ 00:17:35.339 Awaish Kumar: You see, this is a semantic view.
206 00:17:35.510 ⇒ 00:17:43.089 Awaish Kumar: This is where you can create. Basically, you have to select your database, your schema, and you can start creating a view.
207 00:17:43.290 ⇒ 00:17:45.500 Awaish Kumar: But the only thing is that…
208 00:17:45.960 ⇒ 00:17:52.110 Awaish Kumar: We want to create it via, codex code, from the code, instead of creating it from here.
209 00:17:53.670 ⇒ 00:17:54.350 Chris Terry: Okay.
210 00:17:54.350 ⇒ 00:17:58.870 Awaish Kumar: You can click on Create Symetic View, select your fields, it will create it for you.
211 00:18:00.650 ⇒ 00:18:05.730 Awaish Kumar: Right? You’re gonna escape, you’re gonna skip this… phase.
212 00:18:05.910 ⇒ 00:18:09.609 Awaish Kumar: This is to put context. You’re gonna give your name.
213 00:18:09.810 ⇒ 00:18:15.029 Awaish Kumar: Right now, you don’t have the… like, you are on the public role, you don’t have permission to create it.
214 00:18:15.610 ⇒ 00:18:25.710 Awaish Kumar: In the snowflake on the top right, you can see your role that is being used, public. You can change to account admin or something, whatever.
215 00:18:25.840 ⇒ 00:18:28.490 Awaish Kumar: And then you might have access to do it.
216 00:18:29.120 ⇒ 00:18:36.509 Awaish Kumar: Yeah. Now, you see, you have access to create it. Now, you have to choose your name for the semantic view.
217 00:18:36.640 ⇒ 00:18:43.409 Awaish Kumar: And, after giving the name, when you hit next, you are able to create the tables.
218 00:18:44.110 ⇒ 00:18:47.599 Awaish Kumar: Select the tables, and then you have to select the columns, and that’s all.
219 00:18:48.470 ⇒ 00:18:53.439 Awaish Kumar: You will have your semantic view, and then you can, use,
220 00:18:54.390 ⇒ 00:19:02.880 Awaish Kumar: AI chat with your semantic view, like, what is my… I think, like, what are the total…
221 00:19:03.130 ⇒ 00:19:09.089 Awaish Kumar: Fortune 500 companies that attended the CES events, for example.
222 00:19:09.090 ⇒ 00:19:09.610 Chris Terry: Excellent.
223 00:19:10.010 ⇒ 00:19:10.720 Chris Terry: That makes sense.
224 00:19:10.720 ⇒ 00:19:13.410 Awaish Kumar: Yeah, your AI can answer these kind of questions.
225 00:19:13.600 ⇒ 00:19:25.650 Awaish Kumar: But, this is the way to create from UI. The other way is using code. That’s… we can do that once you have the Cortex code installed and set up.
226 00:19:26.490 ⇒ 00:19:28.400 Chris Terry: Okay, understood, understood.
227 00:19:30.790 ⇒ 00:19:40.299 Chris Terry: I don’t know, is that something you could, like, show me what it looks like on that… that side of things? Or you don’t have access, because you’re using a cursor, so you don’t… you don’t really worry about…
228 00:19:40.300 ⇒ 00:19:45.409 Awaish Kumar: Yeah, I don’t have access to Cortex code. We work in cursor. I can show you in cursor how it looks like.
229 00:19:46.000 ⇒ 00:19:47.520 Chris Terry: No, it wouldn’t… it wouldn’t hurt.
230 00:19:47.520 ⇒ 00:19:51.110 Awaish Kumar: But it won’t, like, set your environment, basically.
231 00:19:51.640 ⇒ 00:19:54.539 Chris Terry: Yeah, yeah, understood, understood. We’re gonna be doing something different.
232 00:19:58.100 ⇒ 00:20:05.190 Awaish Kumar: So, you want to set it up right now, or you want to, like, set it up, and maybe we can, again.
233 00:20:05.670 ⇒ 00:20:10.929 Chris Terry: Yeah, I think we’ll have to set up another call, just because I gotta go chat with Catherine about getting access to it.
234 00:20:11.280 ⇒ 00:20:16.339 Awaish Kumar: Yeah, set up codex code only, like, that is the… important thing, I…
235 00:20:17.100 ⇒ 00:20:21.600 Awaish Kumar: maybe ask Kai or Get 3, you know, and then set up codex code.
236 00:20:21.740 ⇒ 00:20:25.589 Awaish Kumar: Because you just have to install it. It’s called Cortex Code.
237 00:20:26.490 ⇒ 00:20:27.060 Chris Terry: Okay.
238 00:20:27.060 ⇒ 00:20:30.179 Awaish Kumar: Next up software, right? It’s just a software like Casser.
239 00:20:31.870 ⇒ 00:20:32.730 Awaish Kumar: And then…
240 00:20:37.500 ⇒ 00:20:43.440 Awaish Kumar: Yeah, you can just say… Cool, thanks.
241 00:20:45.890 ⇒ 00:20:46.770 Chris Terry: I know we did.
242 00:20:46.980 ⇒ 00:20:50.400 Chris Terry: I know we did, like, a CLI the other day for,
243 00:20:50.950 ⇒ 00:20:55.029 Chris Terry: or Snowflake, but I don’t think… I think that might be separate from Cortex, so…
244 00:20:57.600 ⇒ 00:21:06.850 Chris Terry: I’ll probably have to run this one, so I need to chat with her about that as well. But, is that what some of my coworkers are using? Is that what you mentioned?
245 00:21:06.990 ⇒ 00:21:09.560 Chris Terry: Kyle and Catherine are working with.
246 00:21:10.640 ⇒ 00:21:11.380 Awaish Kumar: Sorry?
247 00:21:12.080 ⇒ 00:21:16.299 Chris Terry: So these, the cortex, so that’s what, Kyle and Catherine’s working.
248 00:21:16.630 ⇒ 00:21:19.349 Awaish Kumar: Yeah, everybody in CTA uses that, right?
249 00:21:20.480 ⇒ 00:21:22.030 Chris Terry: Okay, sounds good.
250 00:21:25.610 ⇒ 00:21:26.310 Chris Terry: Yeah.
251 00:21:28.320 ⇒ 00:21:40.410 Chris Terry: So, how would you know, like, because I… we did a lot of stuff yesterday, like, a lot, and I kind of got… I lost track of it. How would I know for a fact that I have access to it? I mean, you know, this cortex is right here to the right.
252 00:21:41.630 ⇒ 00:21:43.090 Awaish Kumar: Yeah, so you’re…
253 00:21:44.600 ⇒ 00:21:51.599 Awaish Kumar: you have access to everything, right? I can see that. You have… you have access to AWS, you have access to Snowflake.
254 00:21:51.780 ⇒ 00:21:56.370 Awaish Kumar: You have access to GitHub repo, you are able to clone it.
255 00:21:57.030 ⇒ 00:22:02.869 Chris Terry: Yeah. Oh, you’re just saying, you can’t see it, so you can see if I have access to it or not, I don’t…
256 00:22:02.870 ⇒ 00:22:08.639 Awaish Kumar: Oh, yeah, like, you just… in the Snowflake, you just clicked on plot marks and different tables.
257 00:22:08.830 ⇒ 00:22:11.899 Awaish Kumar: You are able to see all those tables and things, right?
258 00:22:12.520 ⇒ 00:22:14.659 Chris Terry: Yeah, yeah, I can see all the…
259 00:22:14.660 ⇒ 00:22:15.100 Awaish Kumar: Right.
260 00:22:15.100 ⇒ 00:22:17.029 Chris Terry: Burns to the tables and all that.
261 00:22:17.030 ⇒ 00:22:24.029 Awaish Kumar: Yeah, you are able to see everything. This is all our database. And you also clicked on S3, and we just looked at the raw
262 00:22:24.200 ⇒ 00:22:27.810 Awaish Kumar: Data Lake. So, you have access to the things that we work with.
263 00:22:28.400 ⇒ 00:22:29.009 Chris Terry: Huh.
264 00:22:29.480 ⇒ 00:22:37.780 Awaish Kumar: Yeah, you are able to select different roles, you are able to select account admin, so being an account admin, that means you have everything
265 00:22:38.470 ⇒ 00:22:43.680 Awaish Kumar: So, you have every access in Snowflake. Account admin is the highest level role, basically.
266 00:22:44.300 ⇒ 00:22:49.140 Awaish Kumar: So you have everything in Snowflake. You have access to S3, Asana.
267 00:22:49.450 ⇒ 00:22:53.630 Awaish Kumar: The only thing left right now is… is Cotex code.
268 00:22:54.260 ⇒ 00:22:58.360 Awaish Kumar: So, it’s an ID, you have to set it up locally for your environment.
269 00:22:58.470 ⇒ 00:23:05.289 Awaish Kumar: And once it is done, I can show you how you can develop using code, how can you ask,
270 00:23:05.450 ⇒ 00:23:17.229 Awaish Kumar: Codex Cloud to carry your Snowflake database and return you some rows, or can you ask Codex Code to create a semantic view for you? So it will do everything for you.
271 00:23:19.000 ⇒ 00:23:20.200 Chris Terry: Alright, sounds good.
272 00:23:20.490 ⇒ 00:23:21.040 Awaish Kumar: Yep.
273 00:23:29.680 ⇒ 00:23:31.429 Chris Terry: That’s all. Alright.
274 00:23:31.570 ⇒ 00:23:33.010 Chris Terry: Sounds good, sounds good.
275 00:23:33.370 ⇒ 00:23:37.870 Chris Terry: Yeah, I’ll be chatting with her later today, so I’ll definitely bring that back up.
276 00:23:38.260 ⇒ 00:23:40.410 Chris Terry: Anything else?
277 00:23:40.580 ⇒ 00:23:41.680 Chris Terry: You have in mind?
278 00:23:43.170 ⇒ 00:23:56.900 Awaish Kumar: No, I think that’s all. She, like, she wanted me to, help you with the creating agents, Catherine wanted that, so I can, like, I showed you how to do it in CLI, or sorry, how to do it in UI.
279 00:23:57.130 ⇒ 00:23:57.980 Awaish Kumar: Right?
280 00:23:58.580 ⇒ 00:24:03.939 Awaish Kumar: that was… that is one way to create a catalyst, right? We just saw how to create
281 00:24:04.440 ⇒ 00:24:08.340 Awaish Kumar: Semantic view in, and Snowflake, right?
282 00:24:08.950 ⇒ 00:24:09.620 Chris Terry: Yeah.
283 00:24:10.170 ⇒ 00:24:17.599 Awaish Kumar: This is using UI, you can do that. If you had clicked on further, next, next, we would have ended up creating one.
284 00:24:18.450 ⇒ 00:24:19.000 Awaish Kumar: Cement.
285 00:24:19.000 ⇒ 00:24:21.450 Chris Terry: Okay.
286 00:24:21.450 ⇒ 00:24:26.940 Awaish Kumar: That is… yeah, that is one of the ways. That is by using UI, you can create it.
287 00:24:28.330 ⇒ 00:24:33.410 Awaish Kumar: The other way is, you have to basically create it using,
288 00:24:35.730 ⇒ 00:24:40.600 Awaish Kumar: Yeah, yeah, I want you to call, cortex code.
289 00:24:41.220 ⇒ 00:24:44.209 Awaish Kumar: You can give a name, basically. It depends on what
290 00:24:44.480 ⇒ 00:24:47.929 Awaish Kumar: what view you are creating. Like, if you are creating for
291 00:24:48.520 ⇒ 00:24:54.439 Awaish Kumar: like, Fortune 500, like, the name should be based on what you are trying to do. You can right now create, maybe, test.
292 00:24:55.150 ⇒ 00:24:56.799 Awaish Kumar: Right? Something like that.
293 00:25:00.480 ⇒ 00:25:04.290 Awaish Kumar: Virtual 500 test over here. And then you can drop it after.
294 00:25:05.550 ⇒ 00:25:06.220 Chris Terry: Alright.
295 00:25:06.240 ⇒ 00:25:07.769 Awaish Kumar: Next. Yeah, click next.
296 00:25:07.910 ⇒ 00:25:10.010 Awaish Kumar: You can select the tables.
297 00:25:10.530 ⇒ 00:25:20.840 Awaish Kumar: from here, you want to read… mostly we want to read from prod mods. You can… you have to select the tables that are relevant for this word. So if you’re going to maybe…
298 00:25:21.340 ⇒ 00:25:28.149 Awaish Kumar: CES star, and you can look at the… Fortune 500 table…
299 00:25:29.290 ⇒ 00:25:34.899 Awaish Kumar: If there is an NDM CS425 audit, for example, you can click next here.
300 00:25:37.300 ⇒ 00:25:45.820 Awaish Kumar: So, you can click all the columns, for example, select everything. If… yeah, you can select whatever’s needed, but for now, just select everything and create.
301 00:25:47.400 ⇒ 00:25:50.489 Awaish Kumar: So it basically created a semantic view for you.
302 00:25:52.900 ⇒ 00:25:55.770 Awaish Kumar: So now you can give more information.
303 00:25:55.960 ⇒ 00:26:06.759 Awaish Kumar: you can give custom instruction. If you scroll it down, you have… scroll down, this… yeah, you have derived metrics, you have relationships. If you now want to connect this DIMM table with,
304 00:26:07.050 ⇒ 00:26:14.800 Awaish Kumar: Some other table, some other fact table. You can provide, like, click on the plus side, and then start adding relationships.
305 00:26:15.090 ⇒ 00:26:20.199 Awaish Kumar: Metrics, queries, Verified queries, like, you can add
306 00:26:20.380 ⇒ 00:26:25.530 Awaish Kumar: How to calculate, if you have a query which selects,
307 00:26:26.350 ⇒ 00:26:32.689 Awaish Kumar: the data for something, like Fortune 500 companies, you can give that query here, and
308 00:26:32.810 ⇒ 00:26:34.720 Awaish Kumar: Save, it will have, like.
309 00:26:34.980 ⇒ 00:26:44.109 Awaish Kumar: So the… the AI will know that, okay, this is the query that Chris is using for XYZ. That way, it basically has some…
310 00:26:44.610 ⇒ 00:26:47.210 Awaish Kumar: More context on how to use this data.
311 00:26:47.900 ⇒ 00:26:48.730 Chris Terry: That makes sense.
312 00:26:49.710 ⇒ 00:26:56.899 Awaish Kumar: So this is all you can do using UI, but this is everything that we just saw here, that you can do using code as well.
313 00:26:58.090 ⇒ 00:26:59.329 Chris Terry: Right, right, right, right.
314 00:27:00.330 ⇒ 00:27:11.760 Awaish Kumar: The other thing is streamlined apps. We are also using this, so on the same, again, on the right-hand side… oh, sorry, the left-hand side, if you click on the left-hand side.
315 00:27:12.120 ⇒ 00:27:18.610 Chris Terry: Yeah, let me… let me… let me copy and paste this real quick, so I can keep that separate, or whatever. I know what you’re doing, though. It’s right here.
316 00:27:18.750 ⇒ 00:27:24.499 Awaish Kumar: If you click on stimulate, so now you see, these are all the apps created by our team members.
317 00:27:24.620 ⇒ 00:27:30.330 Awaish Kumar: So, and if you want to create one, you can click on press plus, on the top plus streamlit app.
318 00:27:30.600 ⇒ 00:27:33.280 Awaish Kumar: And if you click here, you can,
319 00:27:33.710 ⇒ 00:27:37.370 Awaish Kumar: You don’t have privileges, because maybe you are using a different role.
320 00:27:37.810 ⇒ 00:27:44.649 Awaish Kumar: Trigger, close it, and if you go back to the left-hand side, On the… on the bottom.
321 00:27:45.890 ⇒ 00:27:47.039 Awaish Kumar: On the bottom.
322 00:27:47.610 ⇒ 00:27:50.050 Awaish Kumar: On the profile URL.
323 00:27:50.380 ⇒ 00:27:51.910 Awaish Kumar: What is your profile name?
324 00:27:52.150 ⇒ 00:27:54.519 Awaish Kumar: Click on your profile name at the last…
325 00:27:56.980 ⇒ 00:27:59.370 Chris Terry: Oh, there it is. Oh, it’s underneath this thing, that’s why it’s.
326 00:27:59.370 ⇒ 00:27:59.700 Awaish Kumar: Yeah.
327 00:27:59.700 ⇒ 00:28:00.310 Chris Terry: Get, getting mad.
328 00:28:00.310 ⇒ 00:28:01.349 Awaish Kumar: Click on this.
329 00:28:03.700 ⇒ 00:28:06.700 Chris Terry: Come on, it won’t go away.
330 00:28:08.320 ⇒ 00:28:09.700 Chris Terry: Alright, there we go.
331 00:28:09.970 ⇒ 00:28:12.080 Awaish Kumar: Yeah. You can see the URL…
332 00:28:12.890 ⇒ 00:28:15.020 Awaish Kumar: Yeah, you have to just change your…
333 00:28:17.220 ⇒ 00:28:18.910 Chris Terry: There we go.
334 00:28:19.930 ⇒ 00:28:26.320 Awaish Kumar: And then you can select your database that you want to select the data from. Oh, sorry, this is for…
335 00:28:26.530 ⇒ 00:28:31.450 Awaish Kumar: Oh, sorry, this is for placing your app, where your app should live.
336 00:28:31.620 ⇒ 00:28:32.650 Awaish Kumar: Basically.
337 00:28:33.530 ⇒ 00:28:36.739 Awaish Kumar: App location. Stimulate apps, maybe you select this.
338 00:28:37.460 ⇒ 00:28:39.829 Awaish Kumar: And you select your schema.
339 00:28:40.020 ⇒ 00:28:46.880 Awaish Kumar: Are you, that, you can select your warehouse that you want to use for this Streamlit app.
340 00:28:47.550 ⇒ 00:28:51.870 Awaish Kumar: for example, warehouse Cortex.
341 00:28:52.290 ⇒ 00:28:56.449 Awaish Kumar: And if you hit create, it will create an empty app for you.
342 00:28:56.810 ⇒ 00:28:57.340 Awaish Kumar: Yeah.
343 00:28:57.340 ⇒ 00:29:00.759 Chris Terry: Question for you. Should you typically do the run on container?
344 00:29:00.960 ⇒ 00:29:02.810 Chris Terry: Or should you run a warehouse?
345 00:29:03.680 ⇒ 00:29:09.399 Awaish Kumar: So… well, I think that’s fine, you can run on the container, and click on Create.
346 00:29:14.420 ⇒ 00:29:18.789 Awaish Kumar: So, it created a… it creates an empty,
347 00:29:20.410 ⇒ 00:29:22.429 Awaish Kumar: app for you. So this is…
348 00:29:22.720 ⇒ 00:29:27.830 Awaish Kumar: Right now, it will have just a template code for you.
349 00:29:28.040 ⇒ 00:29:34.990 Awaish Kumar: That you can… But basically, this is the code for your Steamlet app. Look at the…
350 00:29:35.880 ⇒ 00:29:41.029 Awaish Kumar: You can change it, you can write more code, you can… whatever you want to do with this.
351 00:29:42.230 ⇒ 00:29:45.989 Awaish Kumar: And you can run, and then you can see your final app.
352 00:29:46.450 ⇒ 00:29:47.530 Awaish Kumar: God.
353 00:29:48.920 ⇒ 00:29:59.310 Awaish Kumar: Right now, for example, if you want to explore data from CES, you can write your own code. Like, it’s a Python code, so you just have to…
354 00:30:00.020 ⇒ 00:30:05.960 Awaish Kumar: write more Python code in the bot, add more charts, And more tabs.
355 00:30:06.750 ⇒ 00:30:07.190 Awaish Kumar: tables.
356 00:30:07.190 ⇒ 00:30:07.990 Chris Terry: So you would…
357 00:30:08.520 ⇒ 00:30:15.859 Chris Terry: Yeah, so you could just type that in, like, let’s say, hypothetically, like, over here, and be like, add more charts, or add this, or add that.
358 00:30:15.860 ⇒ 00:30:21.790 Awaish Kumar: You can ask AI to do that, but you can also write yourself. It is just a Python code.
359 00:30:22.060 ⇒ 00:30:22.700 Awaish Kumar: Yeah.
360 00:30:22.700 ⇒ 00:30:23.250 Chris Terry: Yeah.
361 00:30:23.410 ⇒ 00:30:26.399 Awaish Kumar: Then you can click on run, it will run your app.
362 00:30:27.310 ⇒ 00:30:28.190 Awaish Kumar: Yeah.
363 00:30:29.330 ⇒ 00:30:30.499 Chris Terry: Go ahead and click it.
364 00:30:30.920 ⇒ 00:30:33.030 Awaish Kumar: If you click there, it will just show you an app.
365 00:30:38.200 ⇒ 00:30:39.090 Chris Terry: They’re not…
366 00:30:43.220 ⇒ 00:30:46.119 Awaish Kumar: Yeah, I think it’s what is showing here on the…
367 00:30:46.920 ⇒ 00:30:51.850 Chris Terry: Yeah, yeah. It’s already kind of showing. I gotcha, I gotcha. So typically, it would be like this…
368 00:30:52.150 ⇒ 00:30:52.530 Awaish Kumar: Yeah.
369 00:30:52.530 ⇒ 00:30:56.080 Chris Terry: But the fact that this is already made shows here.
370 00:30:56.080 ⇒ 00:31:04.960 Awaish Kumar: Add new charts, it will add here, and then you can share it with external, like, once you are done, you can share it with your team members, with your…
371 00:31:05.730 ⇒ 00:31:07.770 Awaish Kumar: colleagues, and then…
372 00:31:08.630 ⇒ 00:31:18.959 Awaish Kumar: But there is another flow in which we are… we do it using our code, right? In Cortex code, right? You will have your app.
373 00:31:19.910 ⇒ 00:31:27.189 Awaish Kumar: For example, you give a name to this app, and you can ask your codex code, okay, help me update this app.
374 00:31:27.420 ⇒ 00:31:33.430 Awaish Kumar: using, add more charts, you can use Codex Code, like, the AI.
375 00:31:33.550 ⇒ 00:31:43.969 Awaish Kumar: to add more charts for you, to add more things for you, and once it is done, you can ask to deploy. It will deploy to Snowflake. You can come here and verify that
376 00:31:44.100 ⇒ 00:31:49.030 Awaish Kumar: The app looks the way you want it, and once done, you can share it with your team.
377 00:31:49.340 ⇒ 00:31:49.990 Awaish Kumar: So…
378 00:31:49.990 ⇒ 00:31:50.800 Chris Terry: I gotcha.
379 00:31:51.270 ⇒ 00:31:52.309 Awaish Kumar: So that is…
380 00:31:52.470 ⇒ 00:31:56.850 Awaish Kumar: That is a way that we are following right now, is that instead of writing code ourselves, we just
381 00:31:57.200 ⇒ 00:32:01.329 Awaish Kumar: Use our codex code or cursor to give our prompts.
382 00:32:01.970 ⇒ 00:32:07.050 Awaish Kumar: Update the app. Once we’re satisfied, we just deploy it.
383 00:32:07.880 ⇒ 00:32:10.199 Awaish Kumar: And it should end up here, that’s all.
384 00:32:11.050 ⇒ 00:32:12.150 Chris Terry: Yeah, yeah.
385 00:32:12.340 ⇒ 00:32:15.629 Chris Terry: Makes sense. That’s, yeah, that’s a lot. That’s incredible, actually.
386 00:32:16.820 ⇒ 00:32:23.830 Awaish Kumar: Yeah, so everything is… the only thing that you need now is Codex codes, and then set it up, download it.
387 00:32:23.930 ⇒ 00:32:28.029 Awaish Kumar: And once that is done, you can start exploding.
388 00:32:28.220 ⇒ 00:32:30.840 Awaish Kumar: That we discussed today, and then…
389 00:32:30.950 ⇒ 00:32:33.429 Awaish Kumar: Oh, you can set up a new meeting.
390 00:32:33.900 ⇒ 00:32:34.450 Awaish Kumar: with me.
391 00:32:34.450 ⇒ 00:32:35.010 Chris Terry: Yeah, yeah.
392 00:32:35.600 ⇒ 00:32:36.110 Awaish Kumar: Friday.
393 00:32:36.110 ⇒ 00:32:45.479 Chris Terry: I’ll definitely set up some time with you, because I don’t want to accidentally… is there any big no-no’s, like, don’t do this, or don’t do that? Is there anything that I could break, potentially?
394 00:32:46.280 ⇒ 00:32:48.019 Awaish Kumar: Yeah, don’t drop anything.
395 00:32:49.600 ⇒ 00:32:50.940 Chris Terry: I hear that.
396 00:32:51.090 ⇒ 00:32:52.539 Chris Terry: No, that’s fair enough.
397 00:32:54.150 ⇒ 00:33:11.630 Awaish Kumar: Okay, we don’t want you to… like, you can explore data, you can write code, create PRs, but don’t push anything in production, maybe. Like, just create a PR, so everybody knows what you are doing, and it will be reviewed by someone. So, yeah, it’s fine.
398 00:33:14.330 ⇒ 00:33:14.710 Awaish Kumar: Good.
399 00:33:14.710 ⇒ 00:33:22.210 Chris Terry: Catherine was saying that you guys kind of handle what gets pushed, so I should be good. I shouldn’t damage too much stuff, so…
400 00:33:22.510 ⇒ 00:33:30.329 Awaish Kumar: Whatever is pushed, it should be pushed by PRs, and you don’t need to make any change in Snowflake, just use it.
401 00:33:30.930 ⇒ 00:33:35.010 Awaish Kumar: Note changes needed, inside of Snowflake.
402 00:33:35.010 ⇒ 00:33:36.220 Chris Terry: The only thing…
403 00:33:36.220 ⇒ 00:33:39.670 Awaish Kumar: We just create new table and modify our models via…
404 00:33:39.930 ⇒ 00:33:47.780 Awaish Kumar: via DBT, and you should just create a PR for that. So it will be reviewed by somebody then, and it should be totally fine.
405 00:33:47.940 ⇒ 00:33:51.079 Awaish Kumar: Apart from that, there is nothing, yeah.
406 00:33:52.060 ⇒ 00:33:55.579 Chris Terry: That’s fantastic. That was a great explanation. I really appreciate it.
407 00:33:55.950 ⇒ 00:33:57.620 Awaish Kumar: Yeah, no worries. Thank you.
408 00:33:58.350 ⇒ 00:34:08.210 Chris Terry: Yeah, thank you, and we’ll… I’ll reach back out later this week and set something up, once I got the Cortex all, put together.
409 00:34:09.310 ⇒ 00:34:10.139 Awaish Kumar: Thank you.
410 00:34:10.489 ⇒ 00:34:11.699 Chris Terry: Yeah, thank you, sir.
411 00:34:12.020 ⇒ 00:34:12.570 Awaish Kumar: Bye.